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Önal U, Akyol Seyhan D, Ketenoğlu OB, Mert Vahabi M, Başkol Elik D, Memetali SC, Şanlıdağ İşbilen G, Bulut Avşar C, Kaya A, Uyan-Önal A, Yalçın N, Guliyeva G, Dirik Ş, Acet O, Akdağ D, Görür MD, Bozbıyık O, Göktepe B, Gümüş T, Çankayalı İ, Demirağ K, Uyar M, Sipahi H, Erdem HA, Işıkgöz Taşbakan M, Arda B, Aydemir Ş, Ulusoy S, Sipahi OR. Importance of Source Control in the Subgroup of Intra-Abdominal Infections for Septic Shock Patients: Analysis of 390 Cases. Mediterr J Hematol Infect Dis 2024; 16:e2024051. [PMID: 38984090 PMCID: PMC11232688 DOI: 10.4084/mjhid.2024.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 06/14/2024] [Indexed: 07/11/2024] Open
Abstract
Background This study aimed to evaluate the epidemiology of septic shock (SS) associated with intraabdominal infections (IAI) as well as associated mortality and efficacy of early source control in a tertiary-care educational hospital. Methods Patients who had SS with IAI and consulted by Infectious Diseases consultants between December 2013 and October 2022 during night shifts in our centre were analyzed retrospectively. Results A total number of 390 patients were included. Overall, 30-day mortality was 42.5% on day 3, while day 14 and 30 mortality rates were 63.3% and 71.3%, respectively. Source control by surgical or percutaneous operation was performed in 123 of 390 cases (31.5%), and the mortality rate was significantly lower in cases that were performed source control at any time during SS (65/123-52.8% vs 213/267-79.8%, p<0.001). In 44 of 123 cases (35.7%), source control was performed during the first 12 hours, and mortality was significantly lower in this group versus others (24/44-54.5% vs 254/346-73.4%, p=0.009). On the other hand, female gender (p<0.001, odds ratio(OR)= 2.943, 95%CI=1.714-5.054), diabetes mellitus (p= 0.014, OR=2.284, 95%CI=1.179-4.424), carbapenem-resistant Gram-negative etiology (p=0.011, OR=4.386, 95%CI=1.398-13.759), SOFA≥10 (p<0.001, OR=3.036, 95%CI=1.802-5.114), lactate >3 mg/dl (p<0.001, OR=2.764, 95%CI=1.562-4.891) and lack of source control (p=0.001, OR=2.796, 95%CI=1.523-5.133) were significantly associated with 30-day mortality in logistic regression analysis. Conclusion Source control has a vital importance in terms of mortality rates for IAI-related septic shock patients. Our study underscores the need for additional research, as the present analysis indicates that early source control does not manifest as a protective factor in logistic regression.
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Affiliation(s)
- Uğur Önal
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
- Uludag University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bursa, Turkey
| | - Deniz Akyol Seyhan
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
- Kanuni Sultan Süleyman Research and Teaching Hospital, İstanbul, Turkey
| | - Olcay Buse Ketenoğlu
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
| | - Merve Mert Vahabi
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
| | - Dilşah Başkol Elik
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
- Turgutlu State Hospital, Manisa, Turkey
| | - Seichan Chousein Memetali
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
| | - Gamze Şanlıdağ İşbilen
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
| | - Cansu Bulut Avşar
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
- Tınaztepe Hospital, İzmir, Turkey
| | - Arda Kaya
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
| | - Ayse Uyan-Önal
- Yüksek İhtisas Research and Teaching Hospital, Department of Infectious Diseases and Clinical Microbiology, Bursa, Turkey
| | - Nazlıhan Yalçın
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
| | - Günel Guliyeva
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
- Liv Bona Dea Hospital, Baku, Azerbaijan
| | - Şükrü Dirik
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
| | - Oğuzhan Acet
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
| | - Damla Akdağ
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
- Başakşehir Çam and Sakura City Hospital, İstanbul, Turkey
| | - Melike Demir Görür
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
| | - Osman Bozbıyık
- Ege University, Faculty of Medicine, Department of General Surgery, Bornova, Izmir, Turkey
| | - Berk Göktepe
- Ege University, Faculty of Medicine, Department of General Surgery, Bornova, Izmir, Turkey
| | - Tufan Gümüş
- Ege University, Faculty of Medicine, Department of General Surgery, Bornova, Izmir, Turkey
| | - İlkin Çankayalı
- Ege University, Faculty of Medicine, Department of Anesthesiology and Intensive Care, Bornova, Izmir, Turkey
| | - Kubilay Demirağ
- Ege University, Faculty of Medicine, Department of Anesthesiology and Intensive Care, Bornova, Izmir, Turkey
| | - Mehmet Uyar
- Ege University, Faculty of Medicine, Department of Anesthesiology and Intensive Care, Bornova, Izmir, Turkey
| | - Hilal Sipahi
- Bornova Directory of Health, Bornova, Izmir, Turkey
| | - Huseyin Aytac Erdem
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
| | - Meltem Işıkgöz Taşbakan
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
| | - Bilgin Arda
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
| | - Şöhret Aydemir
- Ege University, Faculty of Medicine, Department of Microbiology, Bornova, Izmir, Turkey
| | - Sercan Ulusoy
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
| | - Oguz Resat Sipahi
- Ege University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Bornova, Izmir, Turkey
- King Hamad University Hospital, Bahrain Oncology Center, Department of Oncology Infectious Diseases, AlMuharraq, Bahrain
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Papathanakos G, Blot S, Ho MH. Main determinants of mortality in patients with severe infection or sepsis. Intensive Crit Care Nurs 2024; 81:103614. [PMID: 38154431 DOI: 10.1016/j.iccn.2023.103614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2023]
Affiliation(s)
| | - Stijn Blot
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium.
| | - Mu-Hsing Ho
- School of Nursing, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
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Cheng YW, Kuo PC, Chen SH, Kuo YT, Liu TL, Chan WS, Chan KC, Yeh YC. Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning. J Clin Monit Comput 2024; 38:271-279. [PMID: 38150124 DOI: 10.1007/s10877-023-01108-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 11/15/2023] [Indexed: 12/28/2023]
Abstract
This study applied machine learning for the early prediction of 30-day mortality at sepsis diagnosis time in critically ill patients. Retrospective study using data collected from the Medical Information Mart for Intensive Care IV database. The data of the patient cohort was divided on the basis of the year of hospitalization, into training (2008-2013), validation (2014-2016), and testing (2017-2019) datasets. 24,377 patients with the sepsis diagnosis time < 24 h after intensive care unit (ICU) admission were included. A gradient boosting tree-based algorithm (XGBoost) was used for training the machine learning model to predict 30-day mortality at sepsis diagnosis time in critically ill patients. Model performance was measured in both discrimination and calibration aspects. The model was interpreted using the SHapley Additive exPlanations (SHAP) module. The 30-day mortality rate of the testing dataset was 17.9%, and 39 features were selected for the machine learning model. Model performance on the testing dataset achieved an area under the receiver operating characteristic curve (AUROC) of 0.853 (95% CI 0.837-0.868) and an area under the precision-recall curves of 0.581 (95% CI 0.541-0.619). The calibration plot for the model revealed a slope of 1.03 (95% CI 0.94-1.12) and intercept of 0.14 (95% CI 0.04-0.25). The SHAP revealed the top three most significant features, namely age, increased red blood cell distribution width, and respiratory rate. Our study demonstrated the feasibility of using the interpretable machine learning model to predict mortality at sepsis diagnosis time.
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Affiliation(s)
- Yi-Wei Cheng
- Taiwan AI Labs, Taipei, Taiwan
- Department of Anesthesiology, National Taiwan University Hospital, No. 7, Chung Shan South Road, Taipei, Taiwan
| | - Po-Chih Kuo
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Shih-Hong Chen
- Department of Anesthesiology, Taipei Tzu Chi Hospital, New Taipei, Taiwan
| | - Yu-Ting Kuo
- Department of Anesthesiology, National Taiwan University Hospital, No. 7, Chung Shan South Road, Taipei, Taiwan
| | | | - Wing-Sum Chan
- Department of Anesthesiology, Far Eastern Memorial Hospital, No. 21, Section 2, Nanya S Rd, Banqiao District, New Taipei City, 220, Taiwan.
| | - Kuang-Cheng Chan
- Department of Anesthesiology, National Taiwan University Hospital, No. 7, Chung Shan South Road, Taipei, Taiwan
| | - Yu-Chang Yeh
- Department of Anesthesiology, National Taiwan University Hospital, No. 7, Chung Shan South Road, Taipei, Taiwan.
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Tasleem S, Siddiqui AI, Zuberi MAW, Tariq H, Abdullah M, Hameed A, Aijaz A, Shah HH, Hussain MS, Oduoye MO. Mortality patterns and risk factors in burn patients: A cross-sectional study from Pakistan. BURNS OPEN 2024; 8:13-18. [DOI: 10.1016/j.burnso.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024] Open
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Dejonckheere M, Antonelli M, Arvaniti K, Blot K, CreaghBrown B, de Lange DW, De Waele J, Deschepper M, Dikmen Y, Dimopoulos G, Eckmann C, Francois G, Girardis M, Koulenti D, Labeau S, Lipman J, Lipovestky F, Maseda E, Montravers P, Mikstacki A, Paiva J, Pereyra C, Rello J, Timsit J, Vogelaers D, Blot S. Epidemiology and risk factors for mortality in critically ill patients with pancreatic infection. JOURNAL OF INTENSIVE MEDICINE 2024; 4:81-93. [PMID: 38263964 PMCID: PMC10800767 DOI: 10.1016/j.jointm.2023.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 06/16/2023] [Accepted: 06/23/2023] [Indexed: 01/25/2024]
Abstract
Background The AbSeS-classification defines specific phenotypes of patients with intra-abdominal infection based on the (1) setting of infection onset (community-acquired, early onset, or late-onset hospital-acquired), (2) presence or absence of either localized or diffuse peritonitis, and (3) severity of disease expression (infection, sepsis, or septic shock). This classification system demonstrated reliable risk stratification in intensive care unit (ICU) patients with intra-abdominal infection. This study aimed to describe the epidemiology of ICU patients with pancreatic infection and assess the relationship between the components of the AbSeS-classification and mortality. Methods This was a secondary analysis of an international observational study ("AbSeS") investigating ICU patients with intra-abdominal infection. Only patients with pancreatic infection were included in this analysis (n=165). Mortality was defined as ICU mortality within 28 days of observation for patients discharged earlier from the ICU. Relationships with mortality were assessed using logistic regression analysis and reported as odds ratio (OR) and 95% confidence interval (CI). Results The overall mortality was 35.2% (n=58). The independent risk factors for mortality included older age (OR=1.03, 95% CI: 1.0 to 1.1 P=0.023), localized peritonitis (OR=4.4, 95% CI: 1.4 to 13.9 P=0.011), and persistent signs of inflammation at day 7 (OR=9.5, 95% CI: 3.8 to 23.9, P<0.001) or after the implementation of additional source control interventions within the first week (OR=4.0, 95% CI: 1.3 to 12.2, P=0.013). Gram-negative bacteria were most frequently isolated (n=58, 49.2%) without clinically relevant differences in microbial etiology between survivors and non-survivors. Conclusions In pancreatic infection, a challenging source/damage control and ongoing pancreatic inflammation appear to be the strongest contributors to an unfavorable short-term outcome. In this limited series, essentials of the AbSeS-classification, such as the setting of infection onset, diffuse peritonitis, and severity of disease expression, were not associated with an increased mortality risk.ClinicalTrials.gov number: NCT03270345.
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Affiliation(s)
- Marie Dejonckheere
- Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Massimo Antonelli
- Department of Anesthesiology, Intensive Care and Emergency Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Kostoula Arvaniti
- Intensive Care Unit, Papageorgiou University Affiliated Hospital, Thessaloníki, Greece
| | - Koen Blot
- Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
- Department of Epidemiology and Public Health, Sciensano, Ixelles, Belgium
| | - Ben CreaghBrown
- Surrey Perioperative Anaesthetic Critical Care Collaborative Research Group (SPACeR), Royal Surrey County Hospital, Guildford, UK
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
| | - Dylan W. de Lange
- Department of Intensive Care Medicine, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Jan De Waele
- Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
- Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium
| | - Mieke Deschepper
- Data Science Institute, Ghent University Hospital, Ghent, Belgium
| | - Yalim Dikmen
- Department of Anesthesiology and Reanimation, Cerrahpasa School of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - George Dimopoulos
- 3rd Department of Critical Care, “EVGENIDIO” Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Christian Eckmann
- Department of General, Visceral and Thoracic Surgery, Klinikum Hannoversch-Muenden, Goettingen University, Göttingen, Germany
| | - Guy Francois
- Division of Scientific Affairs-Research, European Society of Intensive Care Medicine, Brussels, Belgium
| | - Massimo Girardis
- Anesthesia and Intensive Care Department, University Hospital of Modena, Modena, Italy
| | - Despoina Koulenti
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- 2nd Critical Care Department, Attikon University Hospital, Athens, Greece
| | - Sonia Labeau
- Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
- Department of Nursing, Faculty of Education, Health and Social Work, University College Ghent, Ghent, Belgium
| | - Jeffrey Lipman
- Jamieson Trauma Institute, The University of Queensland, Brisbane, QLD, Australia
- Nimes University Hospital, University of Montpellier, Nimes, France
| | - Fernando Lipovestky
- Critical Care Department, Hospital of the Interamerican Open University (UAI), Buenos Aires, Argentina
| | - Emilio Maseda
- Surgical Critical Care, Department of Anesthesia, Hospital Universitario La Paz-IdiPaz, Madrid, Spain
| | - Philippe Montravers
- Université de Paris, INSERM, UMR-S 1152-PHERE, Paris, France
- Anesthesiology and Critical Care Medicine, Bichat-Claude Bernard University Hospital, HUPNSV, AP-HP, Paris, France
| | - Adam Mikstacki
- Faculty of Health Sciences, Poznan University of Medical Sciences, Poznan, Poland
- Department of Anaesthesiology and Intensive Therapy, Regional Hospital in Poznan, Poznan, Poland
| | - JoseArtur Paiva
- Intensive Care Department, Centro Hospitalar Universitario S. Joao, Faculty of Medicine, University of Porto, Grupo Infecao e Sepsis, Porto, Portugal
| | - Cecilia Pereyra
- Intensive Care Unit from Hospital Interzonal General de Agudos “Prof Dr Luis Guemes”, Buenos Aires, Argentina
| | - Jordi Rello
- Ciberes and Vall d'Hebron Institute of Research, Barcelona, Spain
| | - JeanFrancois Timsit
- Université Paris-Cité, IAME, INSERM 1137, Paris, France
- AP-HP, Hôpital Bichat, Medical and Infection Diseases ICU (MI2), Paris, France
| | - Dirk Vogelaers
- Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
- Department of General Internal Medicine and Infectious Diseases, AZ Delta, Roeselare, Belgium
| | - Stijn Blot
- Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
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Zhu L, Dou Z, Wu W, Hou Q, Wang S, Yuan Z, Li B, Liu J. Ghrelin/GHSR Axis Induced M2 Macrophage and Alleviated Intestinal Barrier Dysfunction in a Sepsis Rat Model by Inactivating E2F1/NF- κB Signaling. Can J Gastroenterol Hepatol 2023; 2023:1629777. [PMID: 38187112 PMCID: PMC10769719 DOI: 10.1155/2023/1629777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 10/20/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024] Open
Abstract
Sepsis is an inflammatory reaction disorder state that is induced by infection. The activation and regulation of the immune system play an essential role in the development of sepsis. Our previous studies have shown that ghrelin ameliorates intestinal dysfunction in sepsis. Very little is known about the mechanism of ghrelin and its receptor (GHSR) on the intestinal barrier and the immune function of macrophage regulation. Our research is to investigate the regulatory effect and molecular mechanism of the ghrelin/GHSR axis on intestinal dysfunction and macrophage polarization in septic rats. A rat model of sepsis was established by cecal ligation and puncture (CLP) operation. Then, the sepsis rats were treated with a ghrelin receptor agonist (TZP-101) or ghrelin inhibitor (obestatin). The results suggested that TZP-101 further enhanced ghrelin and GHSR expressions in the colon and spleen of septic rats and obestatin showed the opposite results. Ghrelin/GHSR axis ameliorated colonic structural destruction and intestinal epithelial tight junction injury in septic rats. In addition, the ghrelin/GHSR axis promoted M2-type polarization of macrophages, which was characterized by the decreases of IL-1β, IL-6, and TNF-α, as well as the increase of IL-10. Mechanistically, the ghrelin/GHSR axis promoted E2F2 expression and suppressed the activation of the NF-κB signaling pathway in septic rats. Collectively, targeting ghrelin/GHSR during sepsis may represent a novel therapeutic approach for the treatment of intestinal barrier injury.
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Affiliation(s)
- Lei Zhu
- Department of Intensive Care Medicine, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Zhimin Dou
- Department of Intensive Care Medicine, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Wei Wu
- Department of Intensive Care Medicine, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Qiliang Hou
- Department of Intensive Care Medicine, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Sen Wang
- Department of Intensive Care Medicine, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Ziqian Yuan
- Department of Intensive Care Medicine, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Bin Li
- Department of Intensive Care Medicine, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Jian Liu
- Department of Intensive Care Medicine, The First Hospital of Lanzhou University, Lanzhou 730000, China
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Dettori S, Portunato F, Vena A, Giacobbe DR, Bassetti M. Severe infections caused by difficult-to-treat Gram-negative bacteria. Curr Opin Crit Care 2023; 29:438-445. [PMID: 37641512 PMCID: PMC10919274 DOI: 10.1097/mcc.0000000000001074] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
PURPOSE OF REVIEW Antimicrobial resistance (AMR) in Gram-negative bacteria (GNB) poses a significant global health concern, contributing to increased infections, mortality rates, and healthcare costs. This review discusses the main clinical manifestations, therapeutic options, and recent findings in managing antibiotic-resistant GNB, with a focus on difficult-to-treat infections. RECENT FINDINGS Difficult-to-treat resistance (DTR) is a novel classification that identifies GNB exhibiting intermediate or resistant phenotypes to first-line agents in the carbapenem, beta-lactam, and fluoroquinolone categories. The main pathogens implicated in severe infections include DTR Enterobacterales, DTR Pseudomonas aeruginosa , and DTR Acinetobacter baumannii. Although the clinical implications of DTR strains are still under investigation, certain studies have linked them to prolonged hospital stays and poor patient outcomes. SUMMARY Severe infections caused by DTR-GNB pose a formidable challenge for healthcare providers and represent a growing global health issue. The proper administration and optimization of novel antibiotics at our disposal are of paramount importance for combating bacterial resistance and improving patient prognosis.
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Affiliation(s)
- Silvia Dettori
- Infectious Diseases Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neuroscience
| | - Federica Portunato
- Infectious Diseases Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neuroscience
| | - Antonio Vena
- Infectious Diseases Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neuroscience
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Daniele Roberto Giacobbe
- Infectious Diseases Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neuroscience
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Matteo Bassetti
- Infectious Diseases Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neuroscience
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
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8
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Papathanakos G, Andrianopoulos I, Xenikakis M, Papathanasiou A, Koulenti D, Blot S, Koulouras V. Clinical Sepsis Phenotypes in Critically Ill Patients. Microorganisms 2023; 11:2165. [PMID: 37764009 PMCID: PMC10538192 DOI: 10.3390/microorganisms11092165] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/10/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process.
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Affiliation(s)
- Georgios Papathanakos
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Ioannis Andrianopoulos
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Menelaos Xenikakis
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Athanasios Papathanasiou
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Despoina Koulenti
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QL 4029, Australia;
- Second Critical Care Department, Attikon University Hospital, Rimini Street, 12462 Athens, Greece
| | - Stijn Blot
- Department of Internal Medicine & Pediatrics, Ghent University, 9000 Ghent, Belgium;
| | - Vasilios Koulouras
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
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Chang J, Medina M, Kim SJ. Is patients' rurality associated with in-hospital sepsis death in US hospitals? Front Public Health 2023; 11:1169209. [PMID: 37383255 PMCID: PMC10294422 DOI: 10.3389/fpubh.2023.1169209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 05/22/2023] [Indexed: 06/30/2023] Open
Abstract
Background The focus of this study was to explore the association of patients' rurality and other patient and hospital-related factors with in-hospital sepsis mortality to identify possible health disparities across United States hospitals. Methods The National Inpatient Sample was used to identify nationwide sepsis patients (n = 1,977,537, weighted n = 9,887,682) from 2016 to 2019. We used multivariate survey logistic regression models to identify predictors for how patients' rurality is associated with in-hospital death. Findings During the study periods, in-hospital death rates among sepsis inpatients continuously decreased (11.3% in 2016 to 9.9% in 2019) for all rurality levels. Rao-Schott Chi-Square tests demonstrated that certain patient and hospital factors had varied in-hospital death rates. Multivariate survey logistic regressions suggested that rural areas, minorities, females, older adults, low-income, and uninsured patients have higher odds of in-hospital mortality. Further, specific census divisions like New England, Middle Atlantic, and East North Central had greater in-hospital sepsis death odds. Conclusion Rurality was associated with increased in-hospital sepsis death across multiple patient populations and locations. Further, rurality in New England, Middle Atlantic, and East North Central locations is exceptionally high odds. In addition, minority races in rural areas also have an increased odds of in-hospital death. Therefore, rural healthcare requires a more significant influx of resources and should also include assessing patient-related factors.
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Affiliation(s)
- Jongwha Chang
- Department of Pharmaceutical Sciences, Irma Lerma Rangel School of Pharmacy, Texas A&M University, College Station, TX, United States
| | - Mar Medina
- School of Pharmacy, University of Texas at El Paso, El Paso, TX, United States
| | - Sun Jung Kim
- Department of Health Administration and Management, College of Medical Science, Soonchunhyang University, Asan, Republic of Korea
- Center for Healthcare Management Science, Soonchunhyang University, Asan, Republic of Korea
- Department of Software Convergence, Soonchunhyang University, Asan, Republic of Korea
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Jean SS, Liu IM, Hsieh PC, Kuo DH, Liu YL, Hsueh PR. Off-label use versus formal recommendations of conventional and novel antibiotics for the treatment of infections caused by multidrug-resistant bacteria. Int J Antimicrob Agents 2023; 61:106763. [PMID: 36804370 DOI: 10.1016/j.ijantimicag.2023.106763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 02/07/2023] [Accepted: 02/12/2023] [Indexed: 02/19/2023]
Abstract
The infections caused by multidrug- and extensively drug-resistant (MDR, XDR) bacteria, including Gram-positive cocci (GPC, including methicillin-resistant Staphylococcus aureus, MDR-Streptococcus pneumoniae and vancomycin-resistant enterococci) and Gram-negative bacilli (GNB, including carbapenem-resistant [CR] Enterobacterales, CR-Pseudomonas aeruginosa and XDR/CR-Acinetobacter baumannii complex) can be quite challenging for physicians with respect to treatment decisions. Apart from complicated urinary tract and intra-abdominal infections (cUTIs, cIAIs), bloodstream infections and pneumonia, these difficult-to-treat bacteria also cause infections at miscellaneous sites (bones, joints, native/prosthetic valves and skin structures, etc.). Antibiotics like dalbavancin, oritavancin, telavancin and daptomycin are currently approved for the treatment of acute bacterial skin and skin structural infections (ABSSSIs) caused by GPC. Additionally, ceftaroline, linezolid and tigecycline have been formally approved for the treatment of community-acquired pneumonia and ABSSSI. Cefiderocol and meropenem-vaborbactam are currently approved for the treatment of cUTIs caused by XDR-GNB. The spectra of ceftazidime-avibactam and imipenem/cilastatin-relebactam are broader than that of ceftolozane-tazobactam, but these three antibiotics are currently approved for the treatment of hospital-acquired pneumonia, cIAIs and cUTIs caused by MDR-GNB. Clinical investigations of other novel antibiotics (including cefepime-zidebactam, aztreonam-avibactam and sulbactam-durlobactam) for the treatment of various infections are ongoing. Nevertheless, evidence for adequate antibiotic regimens against osteomyelitis, arthritis and infective endocarditis due to several GPC and MDR-GNB is still mostly lacking. A comprehensive review of PubMed publications was undertaken and the formal indications and off-label use of important conventional and novel antibiotics against MDR/XDR-GPC and GNB isolates cultured from miscellaneous sites are presented in this paper.
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Affiliation(s)
- Shio-Shin Jean
- Departments of Internal Medicine and Critical Care Medicine, Min-Sheng General Hospital, Taoyuan, Taiwan; Department of Pharmacy, College of Pharmacy and Health care, Tajen University, Pingtung, Taiwan
| | - I-Min Liu
- Department of Pharmacy, College of Pharmacy and Health care, Tajen University, Pingtung, Taiwan
| | - Po-Chuen Hsieh
- Department of Pharmacy, College of Pharmacy and Health care, Tajen University, Pingtung, Taiwan
| | - Dai-Huang Kuo
- Department of Pharmacy, College of Pharmacy and Health care, Tajen University, Pingtung, Taiwan
| | - Yi-Lien Liu
- Department of Family Medicine, Min-Sheng General Hospital, Taoyuan, Taiwan; Department of Public Health, Taoyuan City Government, Taoyuan, Taiwan
| | - Po-Ren Hsueh
- Departments of Laboratory Medicine and Internal Medicine, China Medical University Hospital, School of Medicine, China Medical University, Taichung, Taiwan; School of Medicine, China Medical University, Taichung, Taiwan; PhD Program for Aging, School of Medicine, China Medical University, Taichung, Taiwan; Departments of Laboratory Medicine and Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.
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11
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Kapp KL, Arul AB, Zhang KC, Du L, Yende S, Kellum JA, Angus DC, Peck-Palmer OM, Robinson RAS. Proteomic changes associated with racial background and sepsis survival outcomes. Mol Omics 2022; 18:923-937. [PMID: 36097965 DOI: 10.1039/d2mo00171c] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Intra-abdominal infection is a common cause of sepsis, and intra-abdominal sepsis leads to ∼156 000 U.S. deaths annually. African American/Black adults have higher incidence and mortality rates from sepsis compared to Non-Hispanic White adults. A limited number of studies have traced survival outcomes to molecular changes; however, these studies primarily only included Non-Hispanic White adults. Our goal is to better understand molecular changes that may contribute to differences in sepsis survival in African American/Black and Non-Hispanic White adults with primary intra-abdominal infection. We employed discovery-based plasma proteomics of patient samples from the Protocolized Care for Early Septic Shock (ProCESS) cohort (N = 107). We identified 49 proteins involved in the acute phase response and complement system whose expression levels are associated with both survival outcome and racial background. Additionally, 82 proteins differentially-expressed in survivors were specific to African American/Black or Non-Hispanic White patients, suggesting molecular-level heterogeneity in sepsis patients in key inflammatory pathways. A smaller, robust set of 19 proteins were in common in African American/Black and Non-Hispanic White survivors and may represent potential universal molecular changes in sepsis. Overall, this study identifies molecular factors that may contribute to differences in survival outcomes in African American/Black patients that are not fully explained by socioeconomic or other non-biological factors.
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Affiliation(s)
- Kathryn L Kapp
- Department of Chemistry, Vanderbilt University, 5423 Stevenson Center, Nashville, TN, 37235, USA.,The Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, 32732, USA.
| | - Albert B Arul
- Department of Chemistry, Vanderbilt University, 5423 Stevenson Center, Nashville, TN, 37235, USA
| | - Kevin C Zhang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA
| | - Liping Du
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.,Vanderbilt Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Sachin Yende
- The Clinical Research, Investigation, and Systems Modeling of Acute Illnesses (CRISMA) Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA.,Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA.,Department of Clinical and Translational Science, University of Pittsburgh, PA, 15261, USA
| | - John A Kellum
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
| | - Derek C Angus
- The Clinical Research, Investigation, and Systems Modeling of Acute Illnesses (CRISMA) Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA.,Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA.,Department of Clinical and Translational Science, University of Pittsburgh, PA, 15261, USA
| | - Octavia M Peck-Palmer
- The Clinical Research, Investigation, and Systems Modeling of Acute Illnesses (CRISMA) Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA.,Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA.,Department of Clinical and Translational Science, University of Pittsburgh, PA, 15261, USA.,Department of Pathology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Renã A S Robinson
- Department of Chemistry, Vanderbilt University, 5423 Stevenson Center, Nashville, TN, 37235, USA.,The Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, 32732, USA.
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Deng Y, Liu S, Wang Z, Wang Y, Jiang Y, Liu B. Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients. Front Med (Lausanne) 2022; 9:933037. [PMID: 36250092 PMCID: PMC9554013 DOI: 10.3389/fmed.2022.933037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 09/01/2022] [Indexed: 11/14/2022] Open
Abstract
Background In-hospital mortality, prolonged length of stay (LOS), and 30-day readmission are common outcomes in the intensive care unit (ICU). Traditional scoring systems and machine learning models for predicting these outcomes usually ignore the characteristics of ICU data, which are time-series forms. We aimed to use time-series deep learning models with the selective combination of three widely used scoring systems to predict these outcomes. Materials and methods A retrospective cohort study was conducted on 40,083 patients in ICU from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Three deep learning models, namely, recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) with attention mechanisms, were trained for the prediction of in-hospital mortality, prolonged LOS, and 30-day readmission with variables collected during the initial 24 h after ICU admission or the last 24 h before discharge. The inclusion of variables was based on three widely used scoring systems, namely, APACHE II, SOFA, and SAPS II, and the predictors consisted of time-series vital signs, laboratory tests, medication, and procedures. The patients were randomly divided into a training set (80%) and a test set (20%), which were used for model development and model evaluation, respectively. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Brier scores were used to evaluate model performance. Variable significance was identified through attention mechanisms. Results A total of 33 variables for 40,083 patients were enrolled for mortality and prolonged LOS prediction and 36,180 for readmission prediction. The rates of occurrence of the three outcomes were 9.74%, 27.54%, and 11.79%, respectively. In each of the three outcomes, the performance of RNN, GRU, and LSTM did not differ greatly. Mortality prediction models, prolonged LOS prediction models, and readmission prediction models achieved AUCs of 0.870 ± 0.001, 0.765 ± 0.003, and 0.635 ± 0.018, respectively. The top significant variables co-selected by the three deep learning models were Glasgow Coma Scale (GCS), age, blood urea nitrogen, and norepinephrine for mortality; GCS, invasive ventilation, and blood urea nitrogen for prolonged LOS; and blood urea nitrogen, GCS, and ethnicity for readmission. Conclusion The prognostic prediction models established in our study achieved good performance in predicting common outcomes of patients in ICU, especially in mortality prediction. In addition, GCS and blood urea nitrogen were identified as the most important factors strongly associated with adverse ICU events.
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Affiliation(s)
- Yuhan Deng
- School of Public Health, Peking University, Beijing, China
| | - Shuang Liu
- School of Public Health, Peking University, Beijing, China
| | - Ziyao Wang
- School of Public Health, Peking University, Beijing, China
| | - Yuxin Wang
- School of Public Health, Peking University, Beijing, China
| | - Yong Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Yong Jiang,
| | - Baohua Liu
- School of Public Health, Peking University, Beijing, China
- *Correspondence: Baohua Liu,
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